Enterprise software application
Understandable Decisions with Vienna, the new version of SMARTS
This blog post presents the latest version of SMARTS, Vienna. Vienna expands SMARTS capabilities by strengthening the ease-of-use and clarity of implementing business decisions, allowing business analysts to more effectively manage complex, automated business decisions. The new version demonstrates continued innovation in the pursuit of Understandable Decisions, an integral part of Understandable AI.
Introduction
At Sparkling Logic, innovation never stops. Version after version, we push the boundaries of simplifying decision management technologies to make it even easier for business people to use them without heavy training in data analytics, machine learning, and business rules. The new version, Vienna, is no exception to the rule: it comes with a multitude of innovations that we group under the name of “understandable decisions.”
After Uhusia, here is Vienna
Since its creation, SMARTS has provided context to business analysts authoring their decision logic. They have been able to instantly see the impact of changes in their business strategies. With the Vienna version, Sparkling Logic has taken this approach to the next level, as their users can now better understand and explain to the team how the decisioning operates step by step. The dual objective is to ensure that the current logic complies with requirements, but also to foster conversation on ways to improve it over time.
To accelerate the implementation and understandability of decisions, Vienna comes with new enhancements and innovations for all the stakeholders.
For business analysts who build the decisioning application, Vienna further simplifies the low-code environment to easily combine data augmentation, pre- and post-data acquisition decisioning, and model operationalization. It also further simplifies the creation, visualization, testing, and debugging of decisions, and therefore reduces errors and biases in decisioning. In addition to these simplifications, Vienna adds new high-level expressions to make decision logic more compact and therefore easy to understand and modify. Decision tables, decision flows, and lookup models have all been affected by these enhancements, enabling projects, large and small, to take full advantage of these improvements.
For business users who operate the decisioning application, Vienna adds dedicated interfaces for them to not only author, but also test, promote, measure, and experiment on their decision logic. With an increasing number of automated tasks within the tool, business users’ productivity rises to higher levels. Business rules can drive the verification and processing of decision logic changes, automatically and seamlessly.
For IT people who first install SMARTS, Vienna comes with new interfaces to connect even more easily to external systems and services, whether on-premises or in the cloud. It also introduces new versions of all the REST decision service invocation SDKs, as well as for the .NET framework and . NET core decision components.
The Vienna beta-testing program has proven to be a success, as customers have provided significant input in the fine-tuning of the new capabilities. Sparkling Logic recognizes and thanks all the companies that actively participated.
Wrap-up of Vienna
- Further simplification of the low-code environment to easily combine data augmentation, pre- and post-data acquisition decisioning, and model operationalization
- Augmented user interface to further simplify the creation, visualization, testing, and debugging of decisions, and therefore reduce errors and biases in decisioning
- New high-level expressions, making decision logic more compact and therefore easy to understand and modify
- Dedicated interfaces for untrained users to author, test, promote, measure, and manage business apps
- New interfaces to connect even more easily to external systems and services, whether on-premises or in the cloud
If you want to learn more about the new version of SMARTS, register for this webinar or contact us for a demo or a free trial.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.
Our motto is “your decisions, our business.” Using SMARTS, organizations have automated complex decisions in days, not weeks, or months. Our mission is to enable customers to implement the most demanding decisioning requirements and to easily maintain and improve them over time.
Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Unlike other tools that focus solely on the authoring and maintenance of business rules, SMARTS is decision-centric and focuses on measuring and improving business outcomes in the context in which our clients work, especially with complex regulations. Major enterprise customers like Equifax, First American, SwissRE, Centene, and NICE Actimize integrate SMARTS in their core systems.
What our customers say about SMARTS
You are about to modernize or develop a new automatic decision-making application, based on data, knowledge, or a combination of both. You wonder whether our SMARTS platform meets your business needs and technical specifications. Nothing is better proof of a product’s superiority than the testimonials of customers. So, we have selected some customers here so you can see why they chose SMARTS as their decision management solution.
Equifax, consumer credit reporting
Equifax is a global data, analytics, and technology company that serves financial institutions, corporations, government agencies, and individuals with enriched data and executable insights. These insights are typically derived from many data sources including financial, telecommunications and utility payments, employment, and income data.Equifax chose SMARTS as a core part of the Equifax InterConnect platform. Credit and risk analysts, both at Equifax and their customers, can seamlessly import data, capture decision logic, A/B test and analyze how the decisions apply to each transaction, and measure the collective impact of making changes to decisions. Business users and business analysts, both at Equifax and their customers, can autonomously make changes to policy rules.
Testimonial
“SMARTS is at the heart of our InterConnect platform. It has helped us quickly enter an era where the credit process must be smooth, automated and optimized for lenders and consumers. SMARTS’ all-in-one approach to authoring, testing and deploying business rules into a sophisticated yet simple product appealed to us from the start. Since then, we have been very satisfied with the use we make of it on a daily basis” — Deepesh Mohandas, Vice President, Global Product Management, Decisioning Platforms.
Learn more about SMARTS at Equifax.
NICE Actimize, suspicious financial activity monitoring
NICE Actimize is the largest and broadest provider of financial crime, risk, and compliance solutions for regional and global financial institutions, as well as government regulators. Consistently ranked as number one in the space, NICE Actimize experts apply innovative technology to protect institutions and safeguard consumers’ and investors’ assets by identifying financial crime, preventing fraud, and providing regulatory compliance.Integrated with NICE Actimize’s platform, SMARTS supports financial institution customers by making more rapid decisions on financial crime strategies and providing the ability to view the overall impact across all NICE Actimize analytics. This capability allows X-Sight customers to make more informed decisions while maximizing financial crime coverage and controlling costs.
Testimonial
“This technology partnership delivers our X-Sight customers more rapid development and deployment of financial crime management strategies across the broader NICE Actimize analytics ecosystem” — Craig Costigan, CEO.
Learn more about SMARTS at NICE Actimize.
Percayso Inform, insurance intelligence
Percayso Inform is an insurance intelligence provider whose services go beyond traditional data enrichment, providing unique, real-time solutions at all stages of the insurance lifecycle and delivers unrivaled insight into insurance customers, risk, and fraud.Powered by SMARTS, Percayso Inform Manager allows insurance providers of all shapes and sizes from start-ups to global insurance businesses across personal and commercial lines to build, adapt and optimize their own data enrichment, rating, and intelligence strategies dynamically and intuitively. It enables high volume data ingestion, rules configuration, operational and strategic decision management as well as a string of valuable features such as reporting, dashboards, champion challenger, data manipulation and more.
Testimonial
“Sparkling Logic stood out from the crowd. The functionality, configurability and ease of use of SMARTS ticked all our boxes but what really impressed us was the genuine desire by their team to create a true partnership and build a foundation from which we could both grow together” — Richard Tomlinson, Managing Director.
Learn more about SMARTS at Percayso Inform.
Enova Decisions, data analytics and decision management
Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses to automate and optimize operational decisions through data, AI, and the cloud- in real-time and at scale.Testimonial
“Leveraging technologies like Sparkling Logic in our cloud service allows our clients to oversee the fine details of the decision algorithm without feeling overburdened by the complexity of what they’re designing” — Sean Naismith, Head of Analytics Services at the time.
Learn more about SMARTS at Enova Decisions.
LTCG, insurance business processing
LTCG is a leading provider of business process outsourcing for the insurance industry. The largest insurers rely on our unparalleled expertise to help manage their complex long-term care portfolios and maximize financial performance.LTCG evaluated tool and platform options and selected SMARTS as the decision engine for their claims adjudication system. They developed and implemented the new system and have since extended their use of SMARTS to support additional business processes.
Testimonial
“Thanks to SMARTS, we were able to discover, test, and deploy automated claims decision logic in under six months” — Kyle Korzenowski, CIO at the time.
Learn more about SMARTS at LTCG.
ABT, power management
ABT Power Management, now part of Concentric, furnishes, engineers, installs, and services industrial batteries and charging systems and is a recognized industry leader in material handling power management.Testimonial
“Now, using SMARTS, we have achieved near real time analysis of this [sensor] data and are able to respond immediately to conditions that need attention. SMARTS’ interface is easy and intuitive enough to allow our engineering staff to create and maintain the rules themselves” — Mike Shemancik, CIO at the time.
Learn more about SMARTS at ABT.
First Rate, wealth management
First Rate is the UK’s largest supplier of foreign currency and a top 5 currency wholesaler globally. They are one of the foremost FX experts in the industry, with a multi-billion-pound wholesale business and over 10 years’ trusted experience providing tailor-made travel money solutions for companies in the finance, travel, and retail sectors.Testimonial
“We chose SMARTS for its comprehensive decision management environment with out-of-the-box integration of business rules, and predictive analytics, and focus on decision improvement” — Nick Collins, Head of Business Solutions at the time.
Learn more about SMARTS at First Rate.
Onlife Health, patient-centric care management
Onlife Health, a GuideWell company, brings simplicity to population health and wellness, connecting and integrating people, technology, and benefit design through a user-friendly engagement platform, guiding members on the “next right thing to do” in their healthcare journey.Testimonial
“We are able to easily change our decisions as business needs dictate and deploy these changes without going through the full software change process.” — David Jarmoluk, Vice President of Enterprise Solutions at the time.
Learn more about SMARTS at Onlife Health.
Do you want to learn more or test SMARTS yourself? Just contact us or request a free trial.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Data vs. knowledge in automated decision management — Why not both?
In the tech industry, we also have our well-known “Coke vs. Pepsi”, “Avis vs. Hertz”, or “Mac vs. PC” debates. In the automated decision management category, the question that keeps coming up is “data vs. knowledge.” The aim of this blog post is to show that from a practical point of view, data and knowledge can be found in the same application. To do this, we will show it with SMARTS, Sparkling Logic’s decision management platform that allows users to combine data and knowledge without them entering the “data vs. knowledge” debate.
Origin of the debate
The “data vs. knowledge” debate dates to an old debate about whether knowledge about a subject should be hand coded or machine learned. A first camp of researchers and practitioners sought to encode this knowledge in the form of rules and an inference engine that runs on these rules to supply answers to user questions. A second camp sought to develop programs that learn from available data using statistical methods to generate models that can make predictions from unseen data. At Sparkling Logic, we support a pragmatic approach that consists in using data, knowledge, or both depending on the problem and the situation at hand.It is all about the situation
There is no such thing as a stand-alone decision management application. It is often built with the purpose of being integrated into a larger system for loan origination, risk management, product configuration, or other similar applications. As I wrote before, there is no one single approach. It is all about the situation.Data is everywhere, easy to collect, organize, and transform into predictive knowledge. So, if you have a lot of data, it may be better to build your decision management application around that data if the new observed data does not deviate too much from the old, learned data.
On the other hand, when you have knowledge whether in the form of rules or procedures, it is better to build your application around this valuable knowledge if it is easy to capture and code into the application.
If you have both data and knowledge, why not using the two, when you can do so in a modern decision management platform such as SMARTS, the subject of the next section.
The SMARTS way
SMARTS is a decision management platform that enables creating, testing, deploying, and improving automated decisions in an integrated platform. I will not detail it here, but you can find a brief overview of SMARTS on our blog page and a full description on our resources page. Instead, I will focus the rest of this article on how to use SMARTS when you have plenty of data or domain knowledge about the application you want to develop.You have plenty of data
For situations where you have plenty of data, SMARTS proposes two tools: RedPen and BluePen.
With RedPen, you write decisions in the form of rules using a use-case driven approach. A loaded data sample supplies the context for the rules and enables immediate execution and testing of each rule. RedPen mimics what subject-matter experts do when they flag decisions.
When you activate RedPen, you can pin an existing rule, a field of this rule, or a rule set and change it as if you were using a real pen on real paper. You can also create new rules with RedPen, SMARTS automatically turns them into executable rules.
On the other hand, BluePen lets you explore and analyze your data using your domain knowledge to find predictors. Then, using these predictors, you can generate a model in the form of legible rules and integrate them into your decision logic.
Using BluePen, you can engineer or change the models any time you need to. Without heavy investment in data analytics tools and efforts, you can evaluate BluePen models in simulations and quickly deploy them in the context of an operational decision.
You have domain knowledge
For situations where you have knowledge, SMARTS proposes two additional tools: SparkL and Pencil.
SparkL is Sparkling Logic’s language for writing rules in a natural language fashion. SparkL comes with everything you need to write rules —mathematical expressions, string manipulations, regular expressions, patterns, dates, logical manipulations, constraints, and much more. You can express any imaginable decision logic and symbolic computation, making it the choice for highly sophisticated decisioning applications where the conditions as well as the actions can take a wide variety of forms.
Pencil is our DMN compliant graphical decision design tool for uncovering, documenting, and sharing decisions with colleagues and partners. With Pencil, you just drag and drop graphical shapes to form a complete decision diagram. Then you add business logic to the graphical shapes and let SMARTS execute it.
Pencil helps you think about the ultimate decisions in a structured way, starting from the top-level decision to smaller sub-decisions. This iterative process is very friendly and amazingly easy to share with colleagues or partners working on the same project.
In addition to the above tools and language, SMARTS comes with a built-in dashboard to measure and improve business outcomes of the taken decisions.
You have both
If you are lucky enough to have both data and knowledge, you can leverage your models’ outputs by using it as the input to rules. For example, your loan management application could run a model that calculates a score and another model that calculates a risk and use that score and risk in a rule to calculate a price.
You can also do it the other way around, using the outputs of rules as inputs to your models that you would have trained with data. For example, your application might run a rule to classify a loan applicant, then run a model to calculate their risk of default and another model to calculate the price.
Whether you have data, knowledge, or both, SMARTS uses them as sources of information for the automation of your operational decisions.
Summary
- Data and knowledge do not have to be antagonistic. They can both be used as inputs to automate decisions.
- SMARTS is a modern decision management platform that enables their combination in an elegant and seamless way. For SMARTS, data and knowledge can be used as sources of information.
- When you have a lot of data, you can use RedPen to write rules without learning a special rule language or syntax, just starting with the data. You can also use BluePen to learn from data and turn it into rules.
- When you have knowledge, you can use SparkL to encode it into rules, from the simplest to the most complex rules that your application may require. You can also use Pencil when designing, documenting, and sharing your decisions are part of the requirements.
- Our mission is to enable customers to implement the most demanding decisioning requirements and to easily change and improve them over time. Whether you have data, knowledge, or both, we can help. Just contact us or request a free trial.
About
Sparkling Logic is a company at the forefront of technological innovation in decision management. We help businesses automate their operational decisions with a powerful decision management platform, designed for business analysts first.Sparkling Logic SMARTSTM (SMARTS for short) is a decision management platform that enables creating, testing, deploying, and improving automated data-based decisions in an integrated easy-to-use environment.
Hassan Lâasri is a data strategy consultant, now leading marketing at Sparkling Logic. You can reach him out at hlaasri@sparklinglogic.com.
Decision Management Systems: What Are They and When to Use Them?
The purpose of this blog post is to succinctly introduce decision management systems for those who are new to the field. First, I present what decision management is, then the technologies that make it up. I also present where they are used and what they bring to the companies that use them.
What is decision management?
There are two types of decision-making technologies. The first are descriptive in that they implement how people make choices among alternatives based on their beliefs and preferences. Think of a doctor deciding a treatment following a diagnosis or a trader buying an asset following a predictive model. The second are normative in that they implement regulations, policies, or strategies regardless of the beliefs or preferences of those who follow the decisions. Think of a loan officer deciding based on an applicant’s repayment history or an insurer calculating the premium an applicant should pay based on the applicant’s medical condition.There is no single definition that differentiates the two technologies. You have heard of knowledge-based systems, expert systems or reinforcement learning for the former technologies and decision tables, decision trees or business rules for the latter. But there is a consensus to name the second type of technologies decision management systems. So, when you hear or read someone referring to decision management, think of prescriptive methods, technologies, products, and systems implementing formal laws, industry regulations, company policies, and business strategies.
What are the underlying technologies?
Often decision management systems are confused with business rule engines, but they are more than that. Behind the terminology of decision management systems hide multiple technologies. The simplest are decision tables, trees, and graphs. The most sophisticated combine rules and predictive models. If we take the example of SMARTS, it integrates eight decision engines into the same platform. Depending on the problem at hand, one may choose one or the other, or even combine them in the same set-up.Also, the users of modern decision management systems are not IT people anymore but businesspeople instead. So, they come with features that allow non-specialists to use them without IT intervention. With modern decision management systems, IT only takes care of the first installations and configurations, the systems come with everything necessary to ensure the governance and security of the applications developed as well as the ease of integrating them into the corporate IT architecture. If we take the example of SMARTS again, it comes with an easy-to-use graphical authoring interface, pre-deployment rule testing, rule repository with version control and rollback, large-scale simulation, real-time decision performance monitoring, and much more.
Where decision management systems are most used?
Like any technology, decision management systems are not a one-size-fits-all solution for every decision problem. They are not suitable for long-term or midterm slow decisions that companies make once a year or a quarter. In these cases, optimization technologies are more used. They are not also suitable for cases with uncertainty and where probabilistic technologies such as probabilistic graphical models are more used.Decision management systems are best suited when there is a substantial number of decisions and calculations that are often nested, often invoked, and likely to change often. Therefore, one must consider decision management systems for the operational and day-to-day decisions that companies make in the thousands and sometimes millions in a single day. We find these cases in banking for credit risk assessment, in insurance for premium calculation and even in retail for product configuration.
Although not dedicated to finance, insurance and healthcare, customers of SMARTS have widely used it for loan origination, risk management, fraud detection and money laundering prevention. These are typically cases where organizations make decisions and calculations thousands and sometimes million times a day and may change based on the market dynamics or global economy, or updates to regulations or business strategy.
What are the key benefits of using decision management systems?
Decision management systems come with two critical benefits. As decisions are explicit, they make it possible to understand and explain the decisions implemented so that one can change them more easily when a new situation requires it. They also reduce the risk of errors and biases met in customer-facing applications, such as recommendation, credit, or insurance systems based solely on machine learning.Key takeaways
- There are two sets of decision-making technologies. The first is descriptive. The second is prescriptive. Decision management systems are descriptive in that they implement formal laws, industry regulations, company policies, or business strategies.
- Decision management systems are best suited when there are thousands or millions of decisions and calculations that are often nested, often invoked, and likely to change often.
- The power of modern decision management systems lies less in the engines but more in the features surrounding them to enable businesspeople take full control of the decisioning process without heavy IT intervention beyond first installation and default configuration.
- Decision management systems have two key benefits. They ease the integration of changes in regulations, policies, and strategies. They also reduce errors and biases in customer-facing applications.
Where to look for further information?
We write regularly about decision management systems and SMARTS. You can find quite a bit of information on our blog and webinars. You can also download our white paper, request a demo of SMARTS, or try it.About
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management platform, accessible to business analysts and ‘citizen developers.’ Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.Sparkling Logic SMARTSTM (SMARTS for short) is an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines business rules with predictive models to create intelligent decisioning systems.
Hassan Lâasri is a data consultant and interim executive, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.
SMARTS for data marketplaces
In my earlier blog post, I explained how decision management and business rules were suitable for micro-calculations, the type of computations that businesses often codify into large spreadsheets and use to score, rate, or price items. In this post, I explain how they are also suitable to simplify data integration, aggregation, and enrichment when building and running data marketplaces.
Data marketplaces
Data marketplaces are a shift from data warehouses where the goal is not only to store large volumes of data, but to make that data be consumed as a service without resorting to IT or prior knowledge of a query language. Data marketplaces are often organized into three layers:- At the lowest layer, we find raw data stored in the form in which it was ingested from the data sources. Data sources can be global ERP and CRM systems, or even local MySQL databases and shared Excel spreadsheets.
- At the middle layer, we find integrated data from multiple sources that is reconciled to resolve disparities and inconsistencies found in the original data. Often, the source systems do not have the same format for dates, names, phone numbers, and addresses. Sometimes the same object can have different attributes in different data sources.
- At the topmost layer, we find aggregate data expressed in summarized forms, often to inform about groups rather than individuals. It is at this level that we also find data enriched by external data to make them directly consumable by the businesspeople.
To learn more about data marketplaces, I recommend the Eckerson Group white paper: The Rise of the Data Marketplace – Data as a Service by Dave Wells.
Easy to define, hard to construct
Defining a data marketplace as we have just done is simple, its construction is complicated for two reasons:1) Data heterogeneity. It is not enough to bring together all the company’s data in a data marketplace for the data to be transformed into knowledge, forecasts, and decisions. Indeed, all data does not have the same age, the same structure, the same format, the same quantity, the same quality, and above all the same utility. If an attribute is important for a business line, it is not automatically important for another business line, yet within the same company.
Each business line has its vision of the product, of the customer, and of any entity managed by the various actors of the company. In the luxury sector for example, a dress, a bag, or a piece of jewelry, although it is a unique object, is seen through different attributes according to the databases where this same object is stored. Looking to exploit all the data available in a company to extract predictions and then decisions not only require integration but also transformation, unification, harmonization, and enrichment.
2) Data reorganization. A data marketplace would work better if data, information, and needs were always stable. But in a dynamic and rapidly changing business world, groups are reorganizing, and companies are acquired, merged or separated. For example, to simplify finance reporting, a country can change the region it was in a year ago, and it’s a safe bet that it will change yet again if a new boss is appointed, or a region is split or added. To be successful, data marketplaces must be implemented as change-tolerant projects.
These two reasons are representative of situations where decision management technologies are used: piecing together things that move independently to each other. Under the name of decision management, we group all the technologies that help organizations to automate all those simple but plentiful granular decisions and calculations that businesses often codify into decision tables, decision trees, or business rules.
Decisioning technologies to the rescue
Decisioning technologies can be used here. But one can use a database programming language or a general-purpose scripting language to automate the loading of data from the data sources into the raw data layer if the original format is kept. There is no real value using a business rules engine to do this straightforward job. The value of using a decisioning technology starts at the integration data layer, where attributes of objects are grouped together to form an updated version of an existing attribute or a new attribute.Take the example of customer data from two different databases. Suppose that customers have their home address and business address in a first source database, and that they only have their home address in a second source database. Suppose also that the format of the addresses is not the same in the two databases. What should the integration data layer hold? An address? So, which one? And what format? Two addresses? So, what professional address to put for these customers who are only present in the second database with the home address? These questions can be easily answered through decision rules.
Now, suppose at the aggregate data layer, we want to add an average turnover with a client that buys from two business units. Here again, calculation rules can be easily used. One can use SMARTS’ look-model engine to automate such calculations.
SMARTS
SMARTS is our all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of micro-decisions and micro-calculations described in this article. SMARTS comes in the form of one product, four capabilities:- A decision management platform that spans the entire life cycle of decisioning from modeling to deployment.
- A low-code no-code environment in which users express decisions and calculations through point-and-clicks and web forms.
- An AI & ModelOps environment that covers the full spectrum of ModelOps from importing existing models, to defining new ones, to executing learning tasks.
- A real-time decision analytics environment to manage the quality of decision and calculation performance.
SMARTS has been extensively used for decisions and calculations in finance, insurance, healthcare, retail, IoT, and utility sectors. To learn more about the product or our references, just contact us or request a free trial.
Wrap-up
- Data marketplaces promise to change the way data is consumed by businesses. Contrary to data warehouses, they are more complex to build and therefore deliver on their promises.
- Decision management technologies simplify data integration, aggregation, and enrichment when building and running data marketplaces. They make decisions and calculations explicit and therefore easy to change whenever situations change.
- SMARTS supplies multiple graphical representations* and engines to make such transformations, integrations, and enrichment easy to design, implement, test, deploy, and change according to situation changes.
* The following table, tree, and graph show three different representations of the same decision logic so that developers can use one that they are familiar with or that best fits the task at hand.
About
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management platform, accessible to business analysts and ‘citizen developers.’ Sparkling Logic SMARTS customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.Sparkling Logic SMARTSTM (SMARTS for short) is an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.
Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.
The SMARTS way for micro-calculations
Until recently, companies used rules to comply with a sectoral regulation, implement a business strategy, or automate a business process. At Sparkling Logic, we were among the few pioneers who helped customers to use business rules for micro-calculations.
Micro-calculations
By micro-calculations (analogy to Michael Ross and James Taylor’s “micro-decisions”), we mean all those simple but plentiful granular calculations that businesses often codify into large spreadsheets and use to score, rate, or price items. Think about it when you click on your smartphone on a digital bank’s app to apply for a loan, or when you go to an auto insurer’s website to find a price for your new car. To you, things seem simple, but behind the scenes there is complexity…Indeed, as for any complex system, it is the interaction between its elements, however simple, that gives rise to its complexity. The price of insurance is not a simple numerical formula but a combination of rules and calculations, all based on data. Data is either provided by you or is internal to the insurance company. A slight change in a rule or a calculation may seem inconsequential at the micro-level but can result in incredibly significant side-effects at the macro-level. Imagine the consequences of a miscalculation in the pricing engine of the insurance company. A single miscalculation could translate into huge losses for the insurance company if the error were not visible in the rule that performs the first micro-calculation but propagates to other rules which use the result of this micro-calculation.
The SMARTS way
Based on a 20+ year experience in implementing or guiding customers to implement sophisticated data-based micro-calculation systems, the founders of Sparkling Logic have developed a way to manage the complexity of such systems. To reduce or even eliminate the potential errors that calculations can generate when modernizing or implementing a new system, they produced the following steps:1) Start with existing data, representing past transactions. If they worked, they should continue working or at least guide the new system. It comes with a built-in engine that automatically turns data spreadsheets into a database which an application can query to perform its micro-calculations.
Take again the example of the auto insurance company. The spreadsheet may contain thousands of past transactions which, depending on the age of the primary driver, the mileage of the car, and other criteria, provides the price for this configuration. But often this price is the same for other configurations, and parts of the configuration at hand appear in other configurations, making direct use of the spreadsheet a complicated exercise. With SMARTS, the user or the application only queries the engine with a configuration and in record time it gives back the corresponding price.
2) Experiment with different representations to visualize the chain of micro-calculations that leads to the final score, rating, or price. SMARTS offers different graphical representations to express a calculation flow: tables, trees and graphs, and rules. Moreover, users can choose and switch between different representations without leaving the graphical user interface. And they can do so until they select the most appropriate representation based on the task at hand as well as the steps that they are familiar with when designing or reviewing the calculation flow.
3) Integrate testing when authoring rules. Decision logic is not software code. As a result, you cannot be satisfied with testing tools and techniques that software developers use. Testing decisions requires a different approach, different techniques, and different tools. Granted, ensuring that your decision service can compile is useful. But the end goal is really to ensure that your decision logic complies with your business objectives. To this end, SMARTS comes with an integrated dashboard where the user can define metrics against which to evaluate the rules —rule by rule, a set of rules, or the entire system.
4) Run A / B simulations. There is no such thing as a timeless business strategy. Economic conditions change suddenly, as does scoring, rating, or pricing. SMARTS allows users to run A / B tests (called Champion / Challenge simulations in credit and risk management) at any time, to evaluate the performance of different strategies, until they find the one that best responds to economic changes. Think of the eligibility criteria, the increase or decrease in prices, and all the parameters that go into the rules and calculations. For these, SMARTS supports big data simulations to experiment with different scenarios of a given strategy, using real data streams.
5) Monitor in real-time. Despite all the care you would put into designing or reviewing your spreadsheets, a typo or error might still not appear until a long time after you put your system into production. To help you manage these cases, SMARTS comes with a real-time decision analytics capability that displays measurements and triggers notifications and alerts when certain KPIs cross thresholds, or the application detects certain patterns. SMARTS pushes notifications by email or generates a ticket in a corporate management system.
Wrap-up
- Decision management and business rules are also suitable for micro-calculations, the type of computations that businesses often codify into large spreadsheets and use to score, rate, or price items.
- A minor change in a rule or a calculation may seem inconsequential at the micro-level but can result in significant side-effects at the macro-level. A single miscalculation could translate into huge losses.
- Sparkling Logic has developed a way to manage the complexity of such systems: Start with existing data, experiment with different representations, integrate testing when authoring rules, run A / B simulations, and monitor in real-time.
If you are planning to upgrade or build a system with micro-calculations, SMARTS can help. The Sparkling Logic team has been involved in projects with scoring / rating / pricing data in the form of large spreadsheets.
Most often, these spreadsheets contained thousands and sometimes tens of thousands of rows. And customers were not just concerned with performance, but also maintainability. The pace of change was high and required additional monitoring and careful management to roll these rates over disparate geographies and time periods. So, just contact us or request a free trial.
Learn more
Implementing Rating Engines with Business Rules and Lookup Models, an online seminar where you will learn how SMARTS manages not only simple rating engines, but also complex pricing engines with a combinatorial explosion of specific cases and continuous evolution over time.
Best-in-class Series: Testing your Decisions, an online seminar to explore what makes decisions different, how to evaluate them, and how to automate regression tests in SMARTS.
About
Sparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful decision management platform, accessible to business analysts and ‘citizen developers.’ Sparkling Logic SMARTS customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is an all-in-one low-code platform for data-driven decision-making. It unifies authoring, testing, deployment, and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.
Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.
Software industry trends behind the digital transformation revolution
This article presents the three software industry trends driving the digital transformation revolution: DevOps, low-code / no-code automation, vertical integration with digital decisioning.
Introduction
The pandemic changed tech priorities for many people both at work and home making a ‘hybrid’ work a top initiative. Where and how we do the work accelerated the need to improve customer digital experiences and efficiency across work, shopping, and everyday chores.The data supports this new trend. The independent research firm Omdia compiled over 300 responses from executives at large companies indicated that working away from traditional offices will become the new norm. 58% percent of respondents said they will adopt a hybrid home/work. Even more interesting is that 68% of enterprises believe employee productivity has improved since the move to remote work.
Similarly, adoption of everyday on-line activities such as shopping, banking and entertainment further accelerated the pace of digital transformation. The need for improved applications increased the pressure on companies to relaunch efficient, friendly front-end customer apps with more intuitive UX. The back end now needs to support faster turnaround with the need to automate processes for the new on-line community of users demanding faster, cleaner, and more intelligent offerings.
To respond to this digital transformation, companies are rapidly adopting easy-to-use integrated enterprise software tools to optimize and accelerate development of these efficient digital products.
Several trends like DevOps, Low-code/automation and vertical integrations with integrated digital decisioning have emerged to help enterprises take the digital transformation journey faster and cheaper.
DevOps
DevOps is a software development concept bringing together historically disconnected functions in the lifecycle of the software development. Traditionally, business analysts would define the problem, developers would interpret the concept and build applications, and operations teams would test, report bugs and provide feedback. The disconnect between the functions, silo’d approach created inefficiencies, increased costs and slowed down application releases.The emergence of integrated tools and processes which integrate this multiple aspect of software development and promote collaboration between these different functions supported growth of the DevOps industry.
In fact, the market data shows that these trends are supported by the investment community and exit activity. According to Venture Beat, in 2Q 2021, Venture funding for global DevOps startups reached $4 billion and the exit activity deal value was dominated by the IPOs of UiPath (robotic process automation) and Confluent, (data / application integration platform).
Low code / no code automation
Application development is also coming closer to non-developers with low/no-code approach and automation.Software engineering, traditionally owned by IT and software engineers, has always been coveted by other, non-IT stakeholders in the enterprise. In 1991, Powerbuilder introduced a revolutionary concept of a development framework, aiming at democratizing development by allowing non-software professionals to get access to application development. Perhaps ahead of its time with clunky UX, WYSIWYG, Powerbuilder started the revolution of introducing emergence of ‘citizen-developers’, people who originally participated alongside IT in shaping the application and business models but could not code and create the applications themselves. It also introduced data integration with application logic and object-oriented concepts like inheritance and polymorphism and encapsulation, bringing software engineering to the masses.
Fast forward to 2020’s, virtually every enterprise tool platform and enterprise customer have adopted a low-code/no-code approach. The mission is the same as 30 years ago – to provide easy to use, graphical UI/UX, drag and drop concept to application development and allow business analysts, ‘citizen-developers’ and non-software engineers to create, test and even deploy enterprise applications.
Vertical integration with digital decisioning
The perennial challenge of allowing non-developers to create applications is the conundrum of how deep they can develop without coding and to what extent they can customize complex enterprise cloud applications without IT and coding.To accelerate digital transformation, enterprise software vendors are emerging mostly from the workflow / BPA world, such as Pega and ServiceNow. They are applying a two prong approach – core tool collection and vertical integration. The workflow vendors have developed (or acquired) a collection of point tools in a core-component framework. Those components typically include AI/ML, reporting, workflow, RPA (Robotic Process Automation), case management, rules engine, decision management, knowledge bases, BPA (business process automation) and process orchestration. Those components typically feature common UI and work across a normalized data model and unified architecture.
But that is not enough. To satisfy modern rapid digital transformation needs, in case of fintech enterprise customers (i.e. banks, insurance companies and financial services) also now require pre-built workflow, data and application models. These vertical templates are higher level and more specific, providing out-of-the box, drag/drop solutions like credit card operations, loan management and payment operations. Using the low-code approach, a business analyst can graphically drag/drop pre-defined steps into a loan origination workflow with pre-defined commonly used tasks, created using best practices defined by the ‘centers of excellence’. Companies like UIPath have created a 3rd party marketplace for additional steps and templates created by analysts and consultants. (Those steps could be ‘get customer data’, ‘OCR input form’, ‘scrub customer data’, authorize user’, ‘assess risk profile’ etc.).
Beyond the top level tasks, the functionality ultimately becomes more complex and the sophisticated customer needs powerful decision capabilities to introduce their own business rules and implement proprietary features. The ‘secret-sauce’, which separtes most common steps from proprietary concepts distinguishes top corporations from the competition, requires more sophisticated digital decisioning tools. These digital decisioning tools enable non-developers to customize and manage decision logic, implement AI/ML features, run A/B testing and visualize performance results on training and production data in real time.
To satisfy most common customer base, digital workflow vendors typically provide rudimentary business rules integrated in their low-code platforms and further integrate them with the downstream workflow platforms and vertical ecosystem vendors (i.e. FiServ, Jack Henry, SAP, Salesforce and FIS in banking for example).
The most sophisticated and demanding customers, however, need a more sophisticated set of digital decisioning tools like standalone professional DM platforms. To simplify and visualize this complex decision management, a new generation of low-code digital decision management platforms like Sparkling Logic emerged. These platforms integrate historical business rules engine, data and AI, demystifying machine-learning and providing low-code approach to development and monitoring of application logic performance, continuously as the business logic and training data change and drift.
The pandemic, hybrid work and pervasiveness of the cloud computing have irreversibly changed the software application development. Enterprise customers are seeking and deploying better, faster, more integrated software tools. DevOps integration, low-code, vertical templates, integrated AI and digital decisioning are becoming a new normal while defining the next generation of applications, created not only by software engineers, but by mere mortals across the enterprise.